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Countless voiceless people sit alone every day and have no one to talk to, people of all ages, who don't feel that they can join any local groups. So they sit on social media all day when they're not at work or school. How can we solve this?

Meaning attaching old SCSI drives to modern Macs, not putting SSDs into old Macs.

Hello HN! I am putting together a community-maintained directory of personal websites at <https://hnpwd.github.io/>. More details about the project can be found in the README at <https://github.com/hnpwd/hnpwd.github.io#readme>.

As you can see, the directory currently has only a handful of entries. I need your help to grow it. If you have a personal website, I would be glad if you shared it here. If your website is hosted on a web space where you have full control over its design and content, and if it has been well received in past HN discussions, I might add it to the directory. Just drop a link in the comments. Please let me know if you do not want your website to be included in the directory.

Also, I intend this to be a community maintained resource, so if you would like to join the GitHub project as a maintainer, please let me know either here or via the IRC link in the README.

By the way, see also 'Ask HN: Could you share your personal blog here?' - https://news.ycombinator.com/item?id=36575081 - July 2023 - (1014 points, 1940 comments). In this post, the scope is not restricted to blogs though. Any personal website is welcome, whether it is a blog, digital garden, personal wiki or something else entirely.

UPDATE: It is going to take a while to go through all the submissions and add them. If you'd like to help with the process, please send a PR directly to this project: https://github.com/hnpwd/hnpwd.github.io

I am curious how people are doing RAG locally with minimal dependencies for internal code or complex documents?

Are you using a vector database, some type of semantic search, a knowledge graph, a hypergraph?

Doesn't matter what domain and how big or small.

With research done till date, what according to you is the best way to provide context to a model. Are there any articles that go into depth of how Cursor does it?

How do context collation companies work?

Trying to view any file gives a unicorn e.g. https://github.com/torvalds/linux/blob/master/README

status is still green: https://www.githubstatus.com/

I have been using Claude Code for DevOps style tasks like SSHing into servers, grepping logs, inspecting files, and querying databases

Overall it's been great. However, I find myself having to review every single command, a lot of which are repetitive. It still saves me a ton of time, but it's quickly becoming a bit tedious

I wish I could give the agent some more autonomy. Like giving it a list of pre-approved commands or actions that it is allowed to run over ssh

For example:

    OK: ls, grep, cat, tail
    Not OK: rm, mv, chmod, etc
    OK: SELECT queries
    Not OK: INSERT, DELETE, DROP, TRUNCATE

What setups have actually worked for you, and where do you draw the line between autonomy and risk?

I am home-schooling my kid. He shares my interest in math and CS, and he's really good at it.

I've been cheering him on a path towards academic success in these 2 fields. In parts because I am not much use at anything else, in parts because he likes it, in parts because that's where I found some measure of success in life.

However, I can't go through a day without reading another article about how AI solved an Erdös problem previously unsolved by humans[1], is getting gold medals at International Mathematical Olympiads[2], is replacing coders at Microsoft[3] and even architects[4].

This makes me really question what I am doing.

Sure, people tell me what matters is training your brain, it's never about the skill itself, but learning to learn, etc... Maybe that's right, but somehow, I can't shake the feeling that I am setting my kid on a path that leads directly into a solid brick wall.

What are the alternatives though?

Play video games all day long and wait for Universal Basic Income to kick in?

Encourage him to pivot towards humanities subject he has no strong interest in, that I would not be great at teaching, and that I've been taught young do not lead to great job opportunities?

Forget math and CS, and teach him how to build and run businesses, banning the reading of any article that shows AI might also be taking this over?

Close my eyes, do not listen to this feeling inside, and continue to teach python generators and linear algebra?

Does anyone have any suggestion, or random comment?

[1]: https://officechai.com/ai/gpt-5-2-and-harmonic-appear-to-have-autonomously-solved-an-erdos-problem-that-had-been-unsolved-by-humans-thus-far/

[2]: https://intuitionlabs.ai/articles/ai-reasoning-math-olympiad-imo

[3]: https://techcrunch.com/2025/04/29/microsoft-ceo-says-up-to-30-of-the-companys-code-was-written-by-ai/

[4]: https://x.com/rakyll/status/2007239758158975130

I ran into a practical limitation while working on ML feature engineering and multi-omics data.

At some point, the problem stops being “how many rows” and becomes “how many columns”. Thousands, then tens of thousands, sometimes more.

What I observed in practice:

- Standard SQL databases usually cap out around ~1,000–1,600 columns. - Columnar formats like Parquet can handle width, but typically require Spark or Python pipelines. - OLAP engines are fast, but tend to assume relatively narrow schemas. - Feature stores often work around this by exploding data into joins or multiple tables.

At extreme width, metadata handling, query planning, and even SQL parsing become bottlenecks.

I experimented with a different approach: - no joins - no transactions - columns distributed instead of rows - SELECT as the primary operation

With this design, it’s possible to run native SQL selects on tables with hundreds of thousands to millions of columns, with predictable (sub-second) latency when accessing a subset of columns.

On a small cluster (2 servers, AMD EPYC, 128 GB RAM each), rough numbers look like: - creating a 1M-column table: ~6 minutes - inserting a single column with 1M values: ~2 seconds - selecting ~60 columns over ~5,000 rows: ~1 second

I’m curious how others here approach ultra-wide datasets. Have you seen architectures that work cleanly at this width without resorting to heavy ETL or complex joins?

After several years building medical device software at ResMed, one thing consistently blocked engineering velocity: documentation.

Not design. Not testing. SOUP assessments, test reports.

For dev teams, this usually means:

Weeks of context switching away from code

Manually rewriting information that already exists in repos, tests, and tickets

Waiting on documentation before releases can move forward (monthly or yearly releases).

We’re launching Qualtate, an AI-powered platform that automates medical device software documentation, starting with:

SOUP documentation

Test reports and evidence generation

In practice, this turns weeks of manual doc work into minutes, using AI to extract, structure, and generate compliant documentation from existing engineering artifacts.

The goal isn’t to “AI-away” quality or compliance. It’s to let developers stay focused on shipping software, while still producing documentation that passes audits.

Regulatory requirements increase every year. AI adoption here feels inevitable. We’re trying to make it useful for engineers—not another tool that slows them down.

We’re opening early access to teams who want to influence what gets automated next.

Happy to answer technical questions, concerns, or skepticism.

Demo on website. https://qualtate.com/

It has been 120 hours (5 days) since the internet shutdown in Iran began. While international phone calls have started working again, data remains blocked.

I am looking for technical solutions to establish resilient, long-term communication channels that can bypass such shutdowns. What are the most viable options for peer-to-peer messaging, mesh networks, or satellite-based solutions that don't rely on local ISP infrastructure?

Google offers a page on https://google.com/url?q=https://news.ycombinator.com/item?id=46613684 that works as an open redirect to any site since at least March 2025 [1].

As such, it often gets used by phishers to piggy-back on the domain reputation of Google by either human actors safety-squinting the domain name or systems that allowlist Google.

Google has often had open redirect problems, for example around AMP, but these seemed to be unintentional and were removed after some time. However, this google.com/url naming scheme almost seems intentional.

This is in contradiction with their own advice (2009) around open redirects [2].

Does anyone know why Google keeps this working, thereby facilitating phishers?

[1] https://www.intego.com/mac-security-blog/scammers-using-new-trick-in-phishing-text-messages-google-redirects/

[2] https://developers.google.com/search/blog/2009/01/open-redirect-urls-is-your-site-being

Is anyone else doing the full software lifecycle for toy projects completely hands off the wheel? I have had Claude running in a Ralph like loop for over 15 hours unsupervised creating over 118 commits.

The technique works like this

while true: if tickets exist -> burn down the backlog by one ticket, exit if not -> figure out what feature would make sense to add next, create PRD and ERD, break down into tickets, exit

It did get stuck once due to tty issues related to running playwright in a non-tty environment but otherwise I have not had to manually step in.

I have it running in a droplet using systemd continuously.

Toy code the agent is creating is a multi-tenant todo kata. Here is the set of prompts:

https://github.com/waynenilsen/ralph-kata-2/tree/main/prompts

Anyone could make their own version of the same, these are just the set of prompts that work for me.

In 15 hours it created a full multi-tenant auth system from scratch and todos with assignees due dates, email reminders, tags and full text search. I created the first PRD by hand with something like "create a PRD for a multi-tenant todo system".

For anyone looking to do something similar, the e2e tests have played a critical role in closing the agent's loop with reality.

The age of programming with prompts is clearly arriving.

Please feel free to submit more than one.

This afternoon I posted some tips on how to present a new* programming language to HN: https://news.ycombinator.com/item?id=46608577. It occurred to me that HN has a tradition of posts called "The {name} programming language" (part of the long tradition of papers and books with such titles) and it might be fun to track them down. I tried to keep only the interesting ones:

https://news.ycombinator.com/thelang

Similarly, Show HNs of programming languages are at https://news.ycombinator.com/showlang.

These are curated lists so they're frozen in time. Maybe we can figure out how to update them.

A few famous cases:

The Go Programming Language - https://news.ycombinator.com/item?id=934142 - Nov 2009 (219 comments)

The Rust programming language - https://news.ycombinator.com/item?id=1498528 - July 2010 (44 comments)

The Julia Programming Language - https://news.ycombinator.com/item?id=3606380 - Feb 2012 (203 comments)

The Swift Programming Language - https://news.ycombinator.com/item?id=7835099 - June 2014 (926 comments)

But the obscure and esoteric ones are the most fun.

(* where 'new' might mean old, of course - https://news.ycombinator.com/item?id=23459210)

What are you working on? Any new ideas that you're thinking about?

Here's a niche request: last year I stumbled upon a personal website on HN, for a topic related to tech. The website also had a section on NYC, mostly Asian, restaurants that was great. I'm trying to find it again but to no avail. The design was fairly minimalist. Does it ring a bell to any one?

I have tried using the API to get all unique URLs on HN for last year, then crawling the websites to find pages matching relevant keywords but to no avail.

I have ADHD. I think. Pretty sure. I have thoughts, ideas, projects, concepts, links, things to read... fired at my brain all day every day. I can go deep on a topic for hours, but then be hit by a barrage of micro ideas. I really struggle to stay on track and focus. Oh and I run a business, manage people, try to make a profit. It's hard. And kids. And life?

I think there is a founder/ADHD thing. Paul Graham thinks so. Maybe even a tech person angle. What have other people experienced?

And how do others cope? I don't really know this world. I do know that my old boss once called me a "flagitating laser beam". I think he meant distracted. I use a bunch of systems to cope. For a long time lists, and then Asana. Asana ruled my life. I just built my own thing to capture tasks, projects, but also knowlegde. Not sure if it will help we will see.

So tell me:

- Who else feels this way? - How do you manage? - Oh and how do you switch off? That is hard

What are some good resources viz. books/papers/articles/videos/etc. to study about the three domains listed above (from Basics to Advanced)?

1) Quantum Computation: What exactly are the abstract models of computation here? Are the Classical Computation models i.e. https://en.wikipedia.org/wiki/Model_of_computation applicable? What other new models have been invented?

2) Quantum Computers: What is the Physics, Organization and Architecture of these? In classical computers you have semiconductor physics, electronic elements and voltage thresholds mapping to logical 1's and 0's. This is then used to build layers of abstractions. What are their equivalents in a quantum computer? https://en.wikipedia.org/wiki/Quantum_computing has a lot of info. but not quite structured for understanding.

3) Quantum Programming: A lot is mentioned at https://en.wikipedia.org/wiki/Quantum_programming and Amazon lists a bunch of books on this topic but am not quite clear on how everything fits. Also as i understand, quantum computing/programming can be simulated on classical hardware but am not clear on the how.

PS: Some detailed examples as to how quantum computers/programming actually help you solve problems which cannot be solved on classical computers would be helpful to bring everything together. Shor's algorithm (https://en.wikipedia.org/wiki/Shor%27s_algorithm) is often mentioned but perhaps starting with a far simpler example would be more accessible.

PPS: In particular; I would love to hear from folks who actually study/research/work in this domain regarding what they actually do, its real-world applicabilities and how to go about learning the subject.

When traversing a public network. Let’s agree going recursive (WireGuard inside VXLAN inside WireGuard) is a bad idea.

We started loading dishes directly into the dishwasher throughout the day. Morning to night. No sink pile.

At night, we run the dishwasher. In the morning, we completely unload it. Empty dishwasher = ready to be used again.

That’s it.

No piles. No guessing whose turn it is. No dishwasher-as-storage. And surprisingly… way fewer fights.

Note before following this system we were washing by hand or bulk load and unload from dishwasher.

Yesterday my production app went down. The cause? DigitalOcean's managed PostgreSQL update broke private VPC connectivity to their managed Kubernetes.

Public endpoint worked. Private endpoint timed out. Root cause: a Cilium bug (#34503) where ARP entries go stale after infrastructure changes.

DO support responded relatively quickly (<12hrs). Their fix? Deploy a DaemonSet from a random GitHub user to ping stale ARP entries every 10 seconds. The upstream Cilium fix is merged but not yet deployed to DOKS. No ETA.

I chose managed services specifically to avoid ops emergencies. We're a tiny startup paying the premium so someone else handles this. Instead, I spent late night hours debugging VPC routing issues in a networking layer I don't control.

HN's usual advice is "just use managed services, focus on the business." Generally good advice. But managed doesn't mean worry-free, it means trading your failure modes for the vendor's failure modes. You're not choosing between problems and no problems. You're choosing between problems you control and (fewer?) problems you don't.

Still using DO. Still using managed services. Just with fewer illusions about what "managed" means.

It has been nearly five years since Ruan Xiaohuan (https://en.wikipedia.org/wiki/Ruan_Xiaohuan) was arrested by Chinese authorities in May 2021. For those unfamiliar, he ran the legendary anonymous blog Program-Think (https://program-think.blogspot.com/) for over a decade.

What haunts me is that his identity was compromised despite his elite background. He was the chief engineer for the 2008 Beijing Olympics network security system. His OPSEC was rigorous: he operated on a cash-only basis, avoided all e-commerce, and never discussed his digital life with anyone, including his wife, who only learned of his "second life" after his disappearance.

Despite his expertise, it's still a mystery how his anonymous persona was deanonymized.

As a hacker in China, I’m interested in your thoughts on the feasibility of maintaining a truly anonymous identity long-term. Is it even possible to win a "battle" where you have to be perfect 100% of the time, while the adversary only needs to find one leak?

What are the most likely failure points in a high-level threat model like this?

Recently, I started to use AI coding agents. They are really great, and I feel like this is the best $100 month I spend for my career.

And yet, I understand that I don’t fully know how they work and what they do behind the scenes. I know the general gist of how an agent works, but I don’t really know if they don’t cat .env behind the scenes, or whether someone on the other side of the planet gets pieces of my code in their AI response.

This is the reason I use AI mainly at $JOB, but not on my personal project (in addition to keeping my skills sharp, and the fun factor). Do you ever think about this? Do you care?

The Linux desktop has spent more than a decade transitioning toward a new graphics stack. Wayland brings many advantages, especially for mobile-style security and simplicity. But in this process, we are quietly losing something valuable: the distributed, protocol‑driven, transport‑agnostic ideas that once made the Linux graphics model unique.

This is not nostalgia. These capabilities matter for remote work, automation, multi‑machine workflows, thin clients, cloud desktops, and future distributed systems. They are not “legacy features”; they are architectural strengths that may become important again.

The problem is not Wayland itself, but the fact that it was never designed to support these use cases. Its philosophy is intentionally local, single‑user, and compositor‑centric. That is perfectly valid for mobile devices, but it leaves a gap for desktop and distributed environments.

Xorg, on the other hand, suffers from an aging implementation, not an outdated philosophy. Its core ideas—protocol‑based rendering, remote execution, composability, and transport independence—remain relevant. What we lack is a modern, minimal, protocol‑only successor that preserves these strengths without carrying Xorg’s historical baggage.

Such a project would not need to replicate Xorg’s entire feature set. It would not need server‑side rendering, fonts, input methods, window management, or security policy. It would simply define a clean, modern protocol and a stable abstraction layer. Existing compositors could implement it. Existing drivers would not need to change. Mesa would not need major redesign. The engineering effort is far smaller than rewriting a full graphics stack.

This is not a call to replace Wayland. It is a call to acknowledge that the Linux desktop may need more than one graphics model. A protocol‑first, implementation‑agnostic layer could coexist with Wayland, complement it, and preserve capabilities that would otherwise disappear.

If no one starts this work, the industry will naturally converge on mobile‑style graphics architectures, and the distributed capabilities of the past may be forgotten for a long time. But if someone begins a modern protocol‑only successor to Xorg, the community may finally have a path that balances simplicity with the flexibility the desktop once had—and may need again.

archive.today has recently (I noticed this, like, 3 days ago) started automatically making requests to someone's personal blog on their CAPTCHA page. Here's a screenshot of what I'm talking about: https://files.catbox.moe/20jsle.png

The relevant JS is:

   setInterval(function() {
     fetch("https://gyrovague.com/?s=" + Math.round(new Date().getTime() % 10000000), {
       referrerPolicy: "no-referrer",
       mode: "no-cors"
     });
   }, 300);

So perhaps this is some kind of revenge/DOS attack attempt/deliberately wasting their bandwidth in response to this article? Maybe an attempt to silence them and force to delete their article? But if it is, then I have so many questions. Like, why would the owner of the archive do that 2.5 years after the article was published? Or why would they even do that in the first place, do they not know about Streisand effect?

I'm confused.

I’m trying to understand a discrepancy between Lichess’s analysis board and my own Stockfish setup.

On Lichess (browser-based analysis), Stockfish reports close to 1 MN/s on my Redmi Note 14 Pro. However, when I run Stockfish locally via a Python program using the native executable, I only see around 600 kN/s.

What’s confusing is that despite the higher reported speed, Lichess takes about 2:30 to reach depth 30, while my local setup reaches depth 30 in about 53 seconds, even though it reports a lower N/s. Lichess also appears much more “active” in terms of frequent evaluation updates.

I suspect this has to do with how N/s is measured or displayed (instantaneous vs average), differences in search configuration (continuous search vs restarts, MultiPV, hash reuse), or overhead from the way the engine is driven (e.g., UI or I/O throttling). It also raises the question of whether “depth 30” is directly comparable across different frontends.

Has anyone looked into how Lichess reports Stockfish speed, or why a setup showing higher N/s can still take significantly longer to reach the same nominal depth?

I’m building an open-source meeting-detection engine (Node + Rust via napi-rs) that uses OS-level signals to detect when meetings start/end.

macOS support is solid, but I’m missing Windows coverage because I don’t have a Windows machine.

I’m looking for contributors to help implement/test:

• process detection (Zoom / Teams / Webex)

• active window/title enumeration

• basic network signals

Repo + docs: https://www.npmjs.com/package/meeting-detection

Happy to break this into small, well-scoped issues.